SageMaker Spider
Project description
SageMaker Spider
AWS SageMaker has a fantastic set of functional components that can be used in concert to setup production level data processing and machine learning functionality.
- Training Data: Organized S3 buckets for training data
- Feature Store: Store/organize 'curated/known' feature sets
- Model Registery: Models with known performance stats/Model Scoreboards
- Model Endpoints: Easy to use HTTP(S) endpoints for single or batch predictions
What is the SageMaker Spider?
- SageMaker is awesome but fairly complex
- Spider lets us setup SageMaker Pipelines in a few lines of code
- Pipeline Graphs: Visibility/Transparency into a Pipeline
- What S3 data sources are getting pulled?
- What Features Store(s) is the Model Using?
- What's the Provenance of a Model in Model Registry?
- What SageMaker Endpoints are associated with this model?
Installation
pip install sagemaker-spider
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
smspider-0.1.1.tar.gz
(988.2 kB
view hashes)
Built Distribution
Close
Hashes for smspider-0.1.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 996953f8a83ef54e68c21b0daab518cddb51878019fbe5a6e1c7d8e7416bb5f3 |
|
MD5 | a0817a962f238999077fbbc166b173fd |
|
BLAKE2b-256 | d0136131b1020eea8c5059960726558e15c09e302eb858534ce50ce0035b3cb0 |